The Costs and Benefits of Mandatory D&O Insurance Policy Disclosure. Dain C. Donelson* McCombs School of Business University of Texas, Austin

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1 ACCOUNTING WORKSHOP The Costs and Benefits of Mandatory D&O Insurance Policy Disclosure By Dain C. Donelson* McCombs School of Business University of Texas, Austin Justin J. Hopkins Darden School of Business University of Virginia Christopher G. Yust McCombs School of Business University of Texas, Austin Thursday, November 13 th, :20 2:50 p.m. Room C06 *Speaker Paper Available in Room 447

2 The Costs and Benefits of Mandatory D&O Insurance Policy Disclosure Dain C. Donelson Justin J. Hopkins Christopher G. Yust November 2014 Key words: Directors and Officers (D&O) Insurance, Litigation, Stock Price Crash Risk, Restatements JEL Codes: K22, M41 Abstract This study examines the costs and benefits of mandatory disclosure of directors and officers (D&O) insurance policies. Using a large, proprietary dataset of D&O policies for U.S. firms, we examine whether policies contain information about future litigation, stock price crashes and restatements. Premiums are related to litigation and restatements, while limits are related to stock price crashes and marginally related to restatements. However, the mandatory disclosure of premiums by firms incorporated in New York appears to attract non-meritorious securities litigation. Overall, mandatory disclosure could provide information about risk but would impose costs. We thank Bill Baber, Jim Blinn, Todd Kravet, Paul Ordyna, Kimberly Reilly, Jake Thornock, David Weber and workshop participants at the University of Connecticut, the University of Virginia (Darden Graduate Business School), Georgetown University and the University of Pennsylvania (Wharton) for helpful comments. We gratefully acknowledge research support provided by the Red McCombs School of Business and the Darden Graduate Business School. Yust gratefully acknowledges financial support from the Deloitte Doctoral Fellowship. All errors are our own.

3 I. Introduction Risk disclosures are a prominent and growing feature of U.S. regulatory policy. In line with this increased focus on risk, some call for the mandatory disclosure of directors and officers (D&O) insurance policy details, including the amount of D&O insurance (the limit) and the amount charged by the insurer (the premium) (e.g. Griffith, 2006). The fact that different jurisdictions apply different disclosure standards highlights the differing views on the topic. For example, Canada and South Korea require disclosure of both the limit and the premium; the state of New York requires disclosure of premiums, but not limits; and many jurisdictions do not require any disclosure. This study examines the potential benefits (revealing information regarding future adverse events) and costs (distorting behavior) of disclosing D&O information. It is important to investigate the implications of mandatory disclosures before a crisis imposes a perceived need for action, a frequent cause (and criticism) of U.S. regulatory action (see Ball, 2009; Hart, 2009; Leuz and Wysocki, 2008). However, this is rarely possible because prospective disclosures are generally not observable prior to the imposition of a federal mandate. We overcome this problem through the combination of a proprietary sample of D&O data for U.S. firms and New York s current mandatory disclosure system. We can thus examine the relation between D&O attributes and outcomes for both firms that currently disclose D&O information and firms that do not in the unique U.S. legal environment. This sample also allows us to provide more direct and comprehensive evidence on the role of D&O insurance for U.S. firms than prior studies, which generally rely on interviews with D&O professionals (e.g., Baker and Griffith, 2006), information disclosed by Canadian firms (e.g., Core, 2000), or small U.S. samples (e.g., Cao and Narayanamoorthy, 2014). We first examine whether D&O policies contain information related to the risk of future adverse outcomes. Prior studies generally focus on the relation between D&O characteristics and 1

4 contemporaneous firm characteristics such as governance (Core, 2000), but do not address the information content of D&O policies for future outcomes. Specifically, we examine whether premiums and limits contain information about the risk of future litigation, stock price crashes and restatements. We select these outcomes because litigation costs are directly covered by D&O insurance, while stock price crashes and restatements are related to litigation. Nearly all securities class actions (the most costly shareholder litigation) are triggered by stock price crashes, and cases with restatements have better plaintiff outcomes (Johnson et al., 2007). D&O premiums likely contain information about future outcomes because insurers demand private information and attempt to set premiums based on litigation risk (Baker and Griffith, 2010). Similarly, managers choose limits based on an inside view of firm risk (Chalmers et al., 2002). However, insurers can also encourage firms to adopt risk mitigation strategies (Cao and Narayanamoorthy, 2014), potentially blurring the relation between premiums and future adverse outcomes. Further, 80% of public companies keep the same coverage levels from year to year (Towers Watson, 2013), suggesting that purchasing decisions may not reflect future risk. In addition, risk averse managers may both select higher limits and be less likely to commit acts that lead to litigation. In summary, it is an empirical question whether D&O limits and premiums contain information about future adverse outcomes. We use a large dataset of D&O policy details for U.S. public firms from This sample along with required control variables encompasses a large segment of the economy, comprising an average of 16% of the market value of the S&P 500 and S&P 1500 each year. This large sample is important in addressing the relation between D&O policy information and infrequent adverse events such as litigation. We examine two time periods that could represent future events, depending on the timing of the D&O disclosure. First, we examine the fiscal 2

5 year covered by the policy. While we refer to this period as current, it would represent future events if disclosure were required when the policy is purchased, for instance by filing an 8-K. Second, we examine the three fiscal years after the year the policy covers. Results based on this definition imply that disclosure would be informative even if it were made after the end of the fiscal year, for instance in the firm s 10-K. We refer to this time period as future because it would represent future events under any disclosure policy. Premiums are strongly associated with current litigation (securities class actions, shareholder derivative claims and SEC enforcement actions) and current restatement announcements, but not stock price crashes. Policy limits are associated with current and future stock price crashes, but are only marginally associated with current restatement announcements and are not associated with litigation. Overall, the results suggest that D&O premiums and limits contain distinct information (incremental to public information) useful for understanding risk. We also explore whether D&O information is more useful for firms with higher litigation risk. In sub-sample analysis, premiums are significantly related to current litigation, and limits are associated with future crashes and current restatements only among firms facing high litigation risk. Quantile regression analysis shows a similar pattern. Premiums are more strongly related to claims and restatements at higher points in the premium distribution. In addition, limits are positively related to future claims at higher points in the limit distribution, and are negatively related to future claims low in the distribution. The negative relation at the bottom of the limit distribution is consistent with risk averse managers at low risk firms purchasing higher levels of insurance compared to similar firms, but also being less likely to engage in risky behavior. Next, we exploit cross-jurisdictional variation in U.S. disclosure requirements to provide evidence on the costs (in the form of any distortion of behavior) from a mandatory disclosure 3

6 system. Given that almost no firms voluntarily disclose D&O information, firms likely perceive the disclosure to be costly (Griffith, 2006; Linck et al., 2008). One potential cost is that the disclosure of premiums (but not limits) might lead firms to purchase less coverage to appear less risky. Contrary to this, we find that firms incorporated in New York have similar coverage limits to firms incorporated in other states using both pooled regressions and matched samples. We also examine whether D&O disclosure increases litigation risk, another potential cost. Plaintiffs might target firms with more insurance, which is the explicit reason that New York firms are required to disclose only premiums (N.Y. Legislature, 1969). However, plaintiffs might still target firms with larger premiums based on the belief that they imply higher limits or higher firm risk. Consistent with this, the positive relation between premiums and litigation is significantly stronger for New York firms in both the current year and future years. It is not clear whether this stronger relation is due to plaintiffs targeting firms with abnormally high premiums; insurers believing that disclosure will encourage litigation and thus charging higher premiums; or more efficient pricing (for example, due to New York firms seeking multiple bids or negotiating for rate reductions). Non-New York firms may pay relatively less attention to D&O premiums given the low cost of such insurance (the median is 0.05% of sales in our sample). While we cannot cleanly distinguish between these alternative explanations, evidence suggests that plaintiffs target firms with high premiums. First, the efficient pricing explanation would imply that New York firms are equally likely to face nonmeritorious litigation when sued as other firms, and that their premiums merely reflect litigation risk more clearly. Inconsistent with this, the stronger relation between premiums and litigation for New York firms is driven entirely by cases that are dismissed. In addition, New York firms do not pay higher overall premiums, inconsistent with insurance pricing driving the difference. 4

7 Thus, it appears that plaintiffs target New York firms with relatively high limits, which are publicly available due to New York s mandatory disclosure policy. This study makes three contributions. First, we provide evidence that D&O policies contain information about future adverse outcomes. Second, we show that premiums and limits provide largely distinct information. These findings imply that disclosure of D&O information could provide important third-party risk assessments. Finally, we provide evidence suggesting that New York s policy of mandatory disclosure leads to non-meritorious litigation for firms with high premiums. Overall, these findings highlight the importance of considering all potential costs and benefits before mandating D&O disclosure at the federal level. Section II provides background information of risk disclosures and D&O insurance. Section III develops testable hypotheses. Section IV describes the data and empirical design, while Section V provides the analysis. Section VI concludes. II. Literature Review and Hypotheses A. Trends in Risk Disclosure Requirements The U.S. Congress, the Financial Accounting Standards Board (FASB), and the SEC have adopted policies requiring or encouraging risk-related disclosures in recent years. For example, the safe harbor protection for forward-looking statements included in the Private Securities Litigation Reform Act of 1995 (PSLRA) was conditioned on firm-specific, nonboilerplate risk disclosure (Schneider and Dubow, 1996). Through the Sarbanes-Oxley Act of 2002, Congress imposed a requirement for management disclosure (and later auditor attestation) regarding the effectiveness of internal controls. The SEC began requiring market risk disclosures in 1998 (Rajgopal, 1999) and disclosure of other firm-specific risk factors in the 10-K in 2005 (Nelson and Pritchard, 2014). The FASB 5

8 has also shown an interest in expanded risk disclosures through FIN 48, which requires disclosure of any liability for tax benefits claimed that are not permitted to be recognized (Mills et al., 2010). In addition, the FASB attempted to improve existing loss contingency disclosures, particularly for litigation. However, commenters expressed concern that increased disclosures would influence the litigation process and the FASB abandoned the project. 1 Overall, there is a clear trend toward additional risk disclosure. A common concern is that risk disclosures will be boilerplate and not reflective of real risk (see Brown et al., 2014). In this respect, D&O premiums could be particularly informative because they reflect independent risk assessments from specialists who have clear economic interests in accurate assessments. In addition, information on limits is less subject to managerial discretion than other risk-related disclosures (such as reporting on internal control weaknesses, see Rice and Weber, 2012) and could thus provide a relatively objective measure of managers beliefs about firm risk. B. D&O Insurance Background Nearly all U.S. public companies purchase D&O insurance. In a recent survey, 193 of 196 public firms indicated that they carried D&O insurance (Towers Watson, 2013). 2 Despite this widespread use, disclosure requirements vary across jurisdictions. In the U.S., only firms incorporated in New York are subject to mandatory disclosure, and they must disclose premiums but not limits. Legislators believed requiring disclosure of limits might unfairly affect the position of an insurer in the defense of a claim or suit (N.Y. Legislature, 1969). The SEC allows D&O policies although it opposes indemnification (D&O policies often 1 The SEC responded by increasing scrutiny of existing contingency disclosures (Whitehouse, 2012). 2 D&O policies include up to three forms of coverage, commonly known as side A, side B and side C coverage. Many firms pay legal costs for directors, known as indemnification. However, some states prohibit indemnification in certain circumstances, such as some derivative claims (e.g., Delaware Code Annotated Title 8, Sec. 145, 2008). Due to the inability to indemnify in certain situations, firms hold side A coverage, which reimburses individuals when the firm is expressly prohibited from indemnification. Side B coverage reimburses firms for costs incurred to indemnify individuals. Side C coverage insures the firm itself against legal claims. While most firms surveyed carry all three forms of coverage (75%), nearly all carry side A and B to protect individuals (Towers Watson, 2013). 6

9 reimburse companies for indemnification payments). 3 Further, the SEC requires disclosure of the existence of a D&O policy for the original issuance of securities. 4 In contrast to U.S. policy, Canada and South Korea require listed firms to disclose aggregate limits and premiums. Similar to the SEC s policy regarding securities issuance, the United Kingdom and China require the disclosure of the existence of D&O policies. Appendix B provides examples of disclosures. III. Hypothesis Development In this section, we develop testable hypotheses based on a mandatory D&O disclosure policy. First, we examine potential benefits based on whether premiums and limits contain incremental risk information. Second, we explore potential costs in terms of distorting behavior. A. D&O Premiums Premiums likely contain incremental information about risk. Insurers have economic incentives to price future litigation risk. Thus, they both demand private information and use publicly available information to set premiums (Baker and Griffith, 2010). For example, Core (2000) finds that premiums are associated with board characteristics and executive compensation, while Cao and Narayanamoorthy (2014) find that premiums are associated with abnormal accruals and prior restatements. 5 Overall, these studies suggest that premiums reflect both past and contemporaneous firm characteristics. However, there are several reasons why D&O premiums may not contain incremental information about future adverse events after controlling for observable firm and stock 3 In particular, if the firm indemnifies officers, the SEC requires disclosing in the registration statement that such indemnification is against public policy as expressed in the Securities Act and is therefore unenforceable (SEC, 1994). See 17 C.F.R (1994). However, the SEC does not penalize firms who purchase D&O insurance. 4 See 17 C.F.R requiring disclosure of the general effect of any statute, charter provisions, by-laws, contract or other arrangements under which any controlling persons, director or officer of the registrant is insured or indemnified in any manner against liability which he may incur in his capacity as such (SEC, 2000). 5 Also, firms caught violating GAAP have strong incentives to strengthen internal controls and governance to prevent future violations (Chakravarthy et al., 2014). Thus, an association between premiums and prior restatements (Cao and Narayanamoorthy, 2014) does not necessarily suggest an association with future restatements. 7

10 characteristics. First, factors unrelated to firm risk affect premiums. For example, in the aftermath of the Enron and WorldCom scandals, insurers raised premiums without regard to individual firm risk (Dobiac, 2007). In addition, insurers rely heavily on public information (Redington, 2005), so D&O information may not contain incremental information after controlling for observable firm characteristics. Finally, insurers may encourage risky firms to adopt strategies to reduce litigation risk (Cao and Narayanamoorthy, 2011). Despite these potential counterarguments, the weight of the evidence predicts that premiums contain information related to future adverse outcomes. We thus examine the following hypothesis: H1: Future litigation, stock price crashes and restatement announcements are positively associated with D&O insurance premiums. B. D&O Limits It is less clear that D&O limits should be associated with future adverse outcomes. Prior studies argue that higher D&O limits shield managers from the disciplining effect of litigation and exacerbate agency issues, either through moral hazard or adverse selection. Chalmers et al. (2002) find a negative relation between IPO coverage levels and three-year post-ipo performance for 72 U.S. companies that went public between 1992 and In addition, they find a relation between limits and litigation in the strict liability environment associated with issuing securities. They conclude that managers opportunistically exploit inside information and purchase D&O policies to protect against litigation when the IPO is overpriced. Lin et al. (2013) find a positive relation between limits and loan spreads among 186 Canadian firms. They also find that future restatements are increasing in D&O limits. However, this literature does not explore whether policy limits contain information about future adverse outcomes in the U.S. legal environment, outside the unique legal regime of IPOs. Managers are likely less proficient at predicting litigation than insurers. As such, current policy 8

11 choices may be based on the same factors that influenced prior decisions (see Towers Watson, 2013). Further, managers may be unwilling to adjust policy coverage in anticipation of future legal troubles as this could provide evidence that managers knew, but failed to timely alert markets, of the event. Finally, managers with higher risk aversion could select higher policy limits while being less likely to take excessive risk. We thus examine the following hypothesis: H2: Future litigation, stock price crashes and restatement announcements and positively associated with D&O insurance limits. C. Potential Disclosure Costs D&O policy disclosure may also have unintended consequences. For example, firms could purchase less insurance to appear less risky. In a partial disclosure regime, such as that of New York that only requires premium disclosure, this could mask the firm s true risk. Similarly, when faced with mandatory reporting of internal control weaknesses, firms had incentives to lower the appearance of risk by investing resources in internal controls (see Coates, 2007). While the net result of such a change may be positive or negative, it represents a distortion of the firm s behavior due to regulation. Thus, we test the following hypothesis: H3: Firms incorporated in the state of New York purchase less D&O insurance than other firms. Another potential cost of disclosure is that it could reveal proprietary information. 6 In the D&O setting, policy information could influence plaintiffs case selection. The legal literature suggests that plaintiffs lawyers in securities class actions seek settlements within D&O policy limits (Cox, 1997). Thus, they could target firms with relatively high limits. In a partial disclosure system, plaintiffs lawyers could assume that abnormally high premiums reflect higher limits or a higher assessment or risk from the insurer (and thus a higher likelihood that any stock 6 Managers frequently make this argument regarding new disclosure requirements. For example, Hughes et al. (2002, 459) report that executives of firms employing [forward] contracts have argued that disclosure of contract terms would reveal proprietary information injurious to their competitive positions. 9

12 price drop or restatement is more likely to be associated with misconduct rather than bad luck). Alternatively, plaintiffs can likely estimate coverage within a reasonable range (Griffith, 2006). If plaintiffs target firms that disclose high premiums, two trends should be observable in the data. First, the premiums of firms incorporated in New York should be more strongly related to litigation than the premiums of firms incorporated elsewhere. Second, because these filings would be driven by disclosures unrelated to actual legal violations, the incidence of nonmeritorious litigation should be higher for firms incorporated in New York. Accordingly, we propose the following hypotheses: H4a: Litigation is more strongly associated with D&O insurance premiums for firms incorporated in New York. H4b: Non-meritorious litigation is more strongly associated with D&O insurance premiums for firms incorporated in New York. IV. Data and Empirical Design A. Sample Description We use D&O insurance data from Advisen, Inc. The data include both the underlying data from the Risk and Insurance Management Society (RIMS) survey and data Advisen collects from insurance brokers and wholesalers. It includes detail by firm and insurance policy, including the policy limit, premium, and whether it is for primary or excess coverage. To match the data to Compustat, we group policies by firm (firms often carry multiple policies) based on the terms (effective date and expiration date), and sum the total limit and premium by fiscal year. 7 Thus, we collapse all firm policies into firm-level aggregate premiums and limits. 8 7 Because policies may not coincide with fiscal years, we assign policies to fiscal years using the Compustat convention. Thus, if the expiration date (the policy year-end) falls between June and December, we assign it to the same fiscal year. If the expiration date is from January through May, we assign it to the prior year. We consolidate policies by fiscal year because the effective dates of policies do not always align cleanly between multiple policies. 8 Based on feedback from data specialists at Advisen, we first remove duplicate observations and obvious errors. We then treat any policy with a term between eleven and thirteen months as if it were a one-year policy. Roughly 95% of the policies have a term of approximately one year that generally correspond with the fiscal year while an 10

13 For our primary sample, we require that firms have D&O insurance data, common stock data available on CRSP, accounting data in Compustat, and restatement data from Audit Analytics. The sample period starts in 1998 because that is the first year D&O data are widely available. The sample ends in 2010 because we require litigation outcomes for some tests and cases are often not resolved for several years. We also test outcomes for three years subsequent to the policy year. The final sample includes 1,544 unique firms, and 5,265 firm-years. B. Measures of Adverse Outcomes We examine the relation between premiums and limits and three measures of adverse outcomes. The first, Claim, is an indicator variable which equals one in the event of litigation and zero otherwise. This is a natural proxy for realized risk because D&O insurance protects individuals and firms against legal penalties. We use comprehensive litigation data from Advisen s MSCAd Large Loss database, which includes securities class actions, derivative actions, and SEC investigations and enforcement actions. The Large Loss database obtains data from sources such as the Stanford Securities Class Action Clearinghouse, Federal agencies, LexisNexis, major law firms, state attorneys general, and other sources. 9 We examine stock price crash risk for two reasons. First, stock price crashes are associated with litigation because investor loss is a component of securities litigation (see Johnson et al., 2007). Second, investors are interested in the ability to predict stock price crashes. We follow Hutton et al. (2009), and create an indicator variable, Stock Price Crash, which equals one when the firm-year observation experiences one or more weekly returns falling 3.09 standard additional 1.5% have a term that is approximately two or three years, combined. The remaining 3.5% of policies either cover a period less than one year or greater than one year but are not two or three year policies. For these policies that are less than a year, we try to append it to an adjacent policy and if none is available, drop the observations. For these policies that are greater than a year but are not two or three year policies, we round the term length down to the nearest year and treat them as if they were two or three year polices, respectively. 9 For information regarding the Large Loss database, see 11

14 deviations below the mean weekly firm return for that fiscal year and zero otherwise. 10 The final measure is the announcement of a non-technical restatement. 11 Restatements are admissions of GAAP violations and are closely associated with litigation (Palmrose and Scholz, 2004). As such, Restatement, is an indicator variable equaling one if the firm announced a restatement during the given fiscal year and zero otherwise. We focus on the announcement, as opposed to the GAAP violation period, because the announcement is the public trigger of stock declines and potential litigation. C. Empirical Design We focus on the two main attributes of firms D&O policies: the premium and limit. We employ the following ordinary least squares (OLS) models, with some variations, to test whether premiums (H1) and limits (H2) contain information about future adverse outcomes (firm and time subscripts are omitted for brevity): 12 Log Premium = β 0 + β 1 Claim Future + β 2 Claim Current + β 3 Claim Past + β 4 Crash Future + β 5 Crash Current + β 6 Crash Past + β 7 Restatement Future + β 8 Restatement Current + β 9 Restatement Past + β 10 Log Limit + β 11 Accrual Quality + β 12 Leverage + β 13 ROA + β 14 MTB + β 15 BigN + β 16 Log Mkt Value + β 17 Prior Loss + β 18 Skew + β 19 Turnover + β 20 Cumulative Return + β 21 Volatility + β 22 Sales Growth + β 23 High Risk + ε (1) Log Limit = β 0 + β 1 Claim Future + β 2 Claim Current + β 3 Claim Past + β 4 Crash Future + β 5 Crash Current + β 6 Crash Past + β 7 Restatement Future + β 8 Restatement Current + β 9 Restatement Past + β 10 Accrual Quality + β 11 Leverage + β 12 ROA + β 13 MTB + β 14 BigN + β 15 Log Mkt Value + β 16 Prior Loss + β 17 Skew + β 18 Turnover + β 19 Cumulative Return + β 20 Volatility + β 21 Sales Growth + β 22 High Risk + ε (2) 10 As a robustness test, we also examine an indicator variable, Significant Drop, that equals one when the firm-year observation experiences a three-day decline less than -10% and more than $100 million in market capitalization and zero otherwise. This alternative stock price crash measure ensures that the crash represents an economically significant loss in both absolute and relative terms. This is important because plaintiffs have incentives to file class actions when damages are large in absolute dollar terms. We find similar results using this variable. 11 While restatements can occur for a variety of technical reasons, Audit Analytics does not include those resulting simply from a change in accounting rules. To further focus on those most damaging to a firm s reputation, we omit any restatements resulting from clerical errors, application failures and those that increase net income. 12 Models 1 and 2 resemble a standard supply-demand relation, suggesting simultaneous estimation. However, it does not appear that D&O limits respond to prices as they would is a standard economic model. The shocks to the D&O industry from the Enron and WorldCom scandals and Sarbanes-Oxley led to premiums more than doubling from 2000 to 2004, but limits did not move appreciably (untabulated). This is likely because managers do not fully internalize the cost of D&O insurance. Nevertheless, results are similar if we use a simultaneous equations approach. 12

15 By using the D&O attributes as the dependent variables, we are able to concurrently examine how past, current and future adverse outcomes are associated with the decisions of insurers (the premium) and managers (the limit). 13 In addition, this design allows us to examine multiple types of adverse outcomes to determine which outcomes are most highly related to D&O policy attributes. This design is similar to that of Piotroski and Roulstone (2005), who examine whether insider trading is associated with both present and future return and cash flow information (Chalmers et al., 2002 use a similar design in the D&O context). An adverse past outcome is defined as one that occurred in the three years prior to the fiscal year covered by the insurance policy. A current outcome is defined as one that occurs during the policy fiscal year, and a future outcome is defined as one that occurs in the three years following the policy fiscal year. As noted, we examine three separate outcomes: Claim, Crash and Restatement. Thus, the coefficients of interest for Claim are β 1 and β 2, which represent the information contained in the D&O attribute about claims in the three years subsequent to the fiscal year (Claim Future ) and during the fiscal year (Claim Current ), controlling for the incidence of claims during the prior three years (Claim Past ) and other information publicly available as of the start of the policy fiscal year. The β 4 and β 5 coefficients provide similar information for Crash, while the β 7 and β 8 coefficients provide similar information for Restatement. 13 Some may find it intuitive to consider the outcome (e.g., litigation) as the dependent variable, but the design we use is more parsimonious as it allows us to test the relation between past, current, and future adverse outcomes for multiple measures of adverse outcomes and the D&O policy attribute in the same model. The design choice to use the D&O policy attribute as the dependent variable does not affect inferences because the t-statistics from the β 1 coefficients in models (1a) and (1b), below, are identical (or vary trivially when standard errors are clustered) when both models are estimated with OLS (see Beaver et al., 1987): D&O Attribute = β 0 + β 1 Outcome Future + β 2 Outcome Current + β 3 Outcome Past + β n Controls + ε Outcome Future = β 0 + β 1 D&O Attribute + β 2 Outcome Current + β 3 Outcome Past + β n Controls + ε While bias can result from reversing a structural equation (Dietrich et al., 2007), it is not clear which direction causation runs in this model: insurers form expectation about future litigation to set premiums, but we present evidence later that premiums may also influence litigation choices by plaintiffs. We thus test only association. Results are also very similar if we use a probit model and separately regress the current (future) outcomes on premiums or limits, controlling for the future (current) and past adverse outcomes along with other controls. 13 (1a) (1b)

16 As noted earlier, the definition of future adverse outcomes would vary based on the timing of disclosure. For example, any information the D&O policy contains about claims that occur during the fiscal year (β 2 ), which we refer to as the current period, would still provide information about future claims if the D&O information is disclosed as a material agreement in an 8-K when the policy is signed. On the other hand, if the D&O policy information is disclosed at the end of the year (for instance, in the 10-K), future-oriented information is limited to what we refer to as future claims (β 1 ). We control for a variety of publicly available firm and return characteristics to ensure that the information in D&O policies is incremental to that already available. Following Kim and Skinner (2012), we include variables that proxy for litigation risk such as: log of market value, market return, return skewness, turnover, volatility, sales growth, and an indicator variable if the firm operates in a high litigation risk industry as defined in Francis et al. (1994). We also control for whether the firm reported negative income in the prior year (Loss), leverage, return on assets (ROA), market-to-book ratio (MTB), and whether the firm is audited by a Big N firm. We also include accrual quality (Dechow and Dichev, 2002) because prior studies find it is the best accounting-based measure of fraud and restatement risk (Jones et al., 2008; Price et al. 2011). We also include industry- and year-fixed effects as litigation risk varies by industry (Kim and Skinner, 2012), and policy premiums vary over time (Fitzpatrick, 2004). All variables are defined in Appendix A and are measured the end of the prior fiscal year or during the 12 months immediately preceding the policy fiscal year. V. Empirical Results A. Descriptive Statistics and Correlations Table 1 provides descriptive statistics of the main variables. The average premium paid is 14

17 approximately $418,000 (log of -0.87). As a benchmark, the mean (median) total premium as a percentage of market value is approximately 0.22 (0.06) percent. Similarly, the mean (median) total premium as a percentage of net sales is approximately 0.42 (0.05) percent. Thus, the relative cost of the premiums to firms is small. 14 However, the premium amount varies widely across the distribution. The premium for the 25th (75th) percentile is approximately $137,000 ($1.2 million). Similarly, coverage limits vary widely across firms. The mean limit is $28.5 million, but the 25th (75th) percentile is $10.0 ($84.8) million. The mean firm has a leverage ratio of 0.76 and has a slightly positive ROA (less than 1%). Approximately a fourth of firms reported a loss in the prior year, and the average firm reports a market-to-book ratio of 2.79, has an equity value of over $720 million, is growing sales, uses a Big N auditor, and does not operate in a high-risk industry. In terms of adverse events, total litigation claims occur at a rate of about 6% per year, stock price crashes occur in about 20% of the fiscal years, but only about 47% of the prior or subsequent three years. Finally, restatements are announced in approximately 5% of fiscal years. Table 2 provides correlations of the main variables. In Panel A, many of the control variables have a high correlation to policy limits and premiums. In particular, log of market value has a 0.71 (0.74) correlation to policy premiums (limits) indicating that size alone accounts for a significant amount of variation in policy attributes. Further, policy limits have a 0.87 correlation to policy premiums. Otherwise, leverage, ROA, market-to-book ratio, the presence of a Big N auditor, and turnover are positively correlated with limits and premiums while accrual quality, the loss indicator variable, returns, return skewness, volatility, sales growth, and the high 14 While total premiums as a percentage of market value or net sales are highly skewed, the log premium variable used in our analysis is not. However, to ensure that our results are not driven by firms that have abnormally high premiums as a percentage of market value or net sales and may be in financial difficulty, in untabulated analysis, we drop all observations that have total premiums as a percentage of market value or net sales greater than 1% (losing approximately 7% of our sample) and the variables are no longer skewed and all inferences are unchanged. 15

18 risk industry indicator variable are negatively correlated with D&O premiums and limits. Panel B provides the correlation between D&O policy limits and premiums and the adverse outcomes such as litigation, restatements, and stock declines. Generally, almost every adverse outcome is positively correlated with D&O limits and premiums, suggesting that D&O attributes are closely related to the incidence of past, current and future adverse outcomes. As examples, premiums are significantly correlated with future (0.18), current (0.17) and past (0.25) litigation claims. Limits are significantly correlated with future (0.15), current (0.13) and past (0.16) litigation claims. Not surprisingly, the strongest correlations are between past adverse outcomes and limits and premiums, indicating that managers (who chose coverage levels) and insurers (who price the policies) are concerned about these particular outcomes. B. Multivariate Tests of the Relation between D&O Attributes and Adverse Outcomes Table 3 provides results regarding the relation between policy attributes and adverse outcomes. Column 1 tabulates the relation between policy premiums and past, current and future claims, stock crashes and restatements. The indicator variable for claims occurring during the fiscal year is positive (0.07) and statistically significant (t = 2.15) while the indicator variable for future claims is positive (0.03) but insignificant (t = 0.92). 15 It may be more challenging for insurers to predict claims at long horizons, and they have the sharpest incentives to price claims filed within the year as that is the period covered by the policy. As would be expected, the coefficient on the prior claim indicator is large (0.28) and statistically significant (t = 8.78) indicating that firms that were the target of litigation in the past three years pay higher premiums for coverage. Economically, this equates to a 32% increase in premiums. Premiums are positively related to the incidence of current restatements with a coefficient of 0.10 (t = 2.59). This is consistent with Palmrose and Scholz (2004), who find a close link 15 Results are similar if we omit Log Limit as a control variable (untabulated). 16

19 between restatements and securities class action litigation. The coefficients on prior and future restatements are positive (0.04 and 0.01, respectively), but only the coefficient on prior restatements is marginally significant (t = 1.30 and 0.37, respectively). Stock price crashes alone are insufficient to merit higher premiums as past, current and future stock price crashes are not associated with premiums. Overall, these results suggest that insurers anticipate future litigation and restatement announcements, although they are more effective at short (one-year) horizons than long (three-year) horizons. This provides some evidence consistent with H1 and concluding that insurance premiums contain information about adverse outcomes. 16 Next, we examine the relation between coverage limits and past, current and future adverse outcomes. The relations between policy limits and outcomes are quite distinct from policy premiums. There are no statistically significant relations between prior, current and future litigation claims and limits, and the coefficient on the future claim and prior claim is negative (-0.01 and respectively). The results with respect to stock price crashes are consistent with managers abilities to forecast future firm-specific events, similar to their ability to earn superior returns on insider trades (e.g., Piotroski and Roulstone, 2005). Alternatively, it could be that D&O coverage induces value eroding risky behavior, consistent with studies suggesting that D&O insurance induces moral hazard (Lin et al., 2011; Lin et al., 2013). 17 Finally, there is only a marginally significant relation between current restatements and limits (t = 1.38). This evidence is somewhat inconsistent with the moral hazard explanation and 16 The model explains a large portion (85%) of the variation in policy premiums. Untabulated tests indicate that the control variables contribute to a large portion of the explanatory power: variables publicly available at the start of the fiscal year (such as the return and accounting variables) explain approximately 66% of the variation in logged premium. Broadly consistent with prior literature (e.g. Cao and Narayanamoorthy, 2014), premiums are positively related to leverage, size, use of a Big N auditor, stock turnover, and volatility. Premiums are negatively related to market-to-book ratio, prior stock return and sales growth. 17 We distinguish between risky behavior that could be desirable to shareholders and that which erodes value. Given that we control for stock volatility in these estimations, the presence of large future stock price crashes is not consistent with volatility, but rather asymmetric price declines. 17

20 is contrary to Lin et al. (2013), who find a strong positive relation between policy limits and current restatements. 18 Overall, we find some evidence consistent with H2 and concluding that insurance limits contain information about adverse outcomes, particularly stock price crashes. 19 Overall, this table demonstrates that policy limits and premiums contain incremental information about future adverse outcomes. Further, each attribute of D&O insurance appears to supply largely distinct information, likely due to different perspectives and incentive functions for managers (who choose policy limits) compared to insurers (who set premiums). Insurers are experienced in evaluating litigation risk, while managers are likely not. Managers may be concerned about the potential for litigation based on stock price crashes because they are unable to predict which crashes will lead to litigation. In addition, coverage levels are largely determined by prior purchasing choices (Towers Watson, 2013). Finally, managers have incentives to over-insure because they benefit from the legal protection provided by the limit but they do not internalize the costs of D&O insurance because the firm pays premiums. We omit governance-related controls from our main model to avoid significant sample size reduction and potential biases in sample composition (see Kim and Skinner, 2012). However, governance characteristics may be correlated both with D&O attributes (e.g., Core, 2000) and future adverse outcomes (e.g., Dechow et al., 1996), so it is important to ensure our results are not driven by correlated omitted variables. In untabulated analyses, we find similar results to Table 3 including controls for the board and audit committee independence percentages, the size of the board, and whether the CEO is also the Chairman. 18 This may be related to sample composition as Lin et al. (2013) analyze 186 Canadian firms. 19 Similar to premiums, the model explains a large portion of the variation in policy limits (64%). Untabulated tests indicate that public information alone explains 63% of the variation in coverage limits. Further, the relations between the control variables and policy limits are nearly identical to the relations with policy premiums. Leverage, the use of a Big N auditor, market value, and volatility are all positively related to limits while the market-to-book ratio, skewness, return, the high risk indicator variable and sales growth are all negatively related to limits. 18

21 C. Additional Analyses on Benefits of Disclosure Since we observe that both premiums and limits contain incremental information about future adverse outcomes on average, we examine whether the information content varies crosssectionally according to litigation risk. While some firms regularly face high litigation risk, other firms face minimal litigation risk (Nelson and Pritchard, 2014). As such, insurers have strong economic incentives to accurately assess risk in firms with higher litigation risk, and relatively lower incentives for low risk firms. To examine whether the information content of D&O disclosure varies based on litigation risk, we estimate a firm-specific measure following Kim and Skinner (2012) and rank firm-years into terciles based on estimated litigation risk. Table 4 presents the results of separately estimating models 1 and 2 for high litigation risk firms (firms in the top tercile of litigation risk) and low litigation risk firms (firms in the bottom tercile of litigation risk). Columns 1 and 2 tabulate the relation between policy premiums and past, current and future claims, stock crashes and restatements for high and low litigation risk firms, respectively. The coefficient on the indicator variable for claims occurring during the fiscal year for high litigation risk firms is positive (0.09) and statistically significant (t = 2.01) while the coefficient is insignificant for low litigation risk firms. Thus, premiums are particularly informative about litigation risk for the very firms for which investors would be most concerned about litigation risk. The indicator variable for current restatements is positive and statistically significant for both sub-samples. Similar to the main results, the indicator variables for stock price crash risk are insignificant in both sub-samples. Columns 3 and 4 tabulate the relation between policy limits and past, current and future claims, stock crashes and restatements for high and low litigation risk firms, respectively. The coefficient on the indicator variable for future stock price crash risk is only positive and 19

22 statistically significant for the high litigation risk firms. Similarly, the coefficient on the indicator variable for current restatements is only positive and significant for high litigation risk firms. Collectively, while there is some benefit to D&O disclosure even for the low litigation risk firms, the information content in D&O attributes is concentrated in the high litigation risk firms. Additionally, we examine whether the information content in D&O attributes varies nonlinearly based on the size of the premium and limit. To analyze this issue, we perform quantile regression for each quintile of the D&O attribute s distribution (i.e., 0.20, 0.40, etc). Table 5 presents results. Premiums are most informative about current and future litigation in the top two quintiles of the distribution. This is logical as significantly higher premiums, controlling for differences in limits, indicate a significantly higher perception of litigation risk by D&O insurers. However, premiums contain information about current restatements over the entire distribution. When the relation between limits and claims is allowed to vary, limits also contain information about claims. Limits in the bottom quintile of the distribution are negatively associated with future claims while the top two quintiles are positively associated with future claims. Limits are positively related to the future crash risk indicator over their entire distribution. Overall, these tests provide consistent evidence across both sub-sample sorts and quantile regression that D&O policy information is most informative among high litigation risk firms. D. Costs of Mandatory Disclosure We next use the partial disclosure regime in New York to explore two potential costs to a mandatory disclosure policy. Specifically, we examine whether New York s mandatory disclosure policy distorts firm purchasing behavior (H3) and whether plaintiffs use these disclosures to target potential defendants (H4a and H4b). We first test whether disclosure of D&O information distorts firms purchasing 20

23 decisions. 20 In particular, if managers believe that the market interprets a high premium as evidence of risk, then they might purchase less insurance to reduce premiums. Alternatively, if managers do not hold such beliefs, or the importance of sufficient coverage outweighs expected market penalties, New York s disclosure requirements should have no effect on insurance limits. Columns 1 and 2 of Table 6 provide these test results. We find no significant relation between New York incorporation and policy limits in both a parsimonious model controlling only for firm size, and a model with control variables as well as industry- and yearly-fixed effects. In untabulated tests, we also conduct a variety of other tests such as matching firms based on a variety of different control sets, and also examine only firms headquartered in the state of New York (some of which are incorporated in New York and some of which are not). In all tests, the disclosure requirement has no statistically significant relation with the amount of D&O insurance coverage that firms purchase. Overall, these results are inconsistent with H3 and imply that disclosure does not affect coverage levels. Table 3 showed that higher premiums are positively associated with current litigation. We next examine whether this relation between policy premiums and current litigation is stronger for firms required to disclose premiums. This would be the case if plaintiffs target firms with high premiums under the assumption that these firms carry higher levels of insurance. We focus on securities class actions for this test because they result in the largest settlements, and plaintiffs lawyers try to maximize settlements through case selection (Coffee, 2006). 21 Accordingly, in columns 1 and 2 of Table 7, we separately estimate model 1for securities class action litigation for New York and non-new York firms, controlling for other variables as in model 1. The coefficient on the indicator variable for current securities class action litigation 20 The prior analyses assume that information in D&O policies would not change if disclosure were mandatory. 21 Results are similar using all claims (untabulated). 21